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Multi-agent Approach to the DVRP with GLS Improvement Procedure

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Intelligent Decision Technologies (IDT 2020)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 193))

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Abstract

Vehicle Routing Problem (VRP) class refers to a wide range of transportation problems where a set of vehicles have to deliver (or pickup) goods or persons to (from) locations situated in a given area. The Dynamic Vehicle Routing Problem (DVRP) class generalizes the VRP by assuming that information about customers is not given a priori to the decision-maker and it may change during over the time. It means that at any moment of time, there may exist customers already under servicing and new customers which need to be serviced. As a consequence, each newly arriving request needs to be incorporated into the existing vehicles tours, which means that the current solution may need to be reconfigured to minimize the goal functions. The paper presents a multi-agent approach to the DVRP, where Guided Local Search (GLS) procedure has been applied to periodic re-optimization of static subproblems, including requests, which have already arrived to the system. Computational experiment has been carried out to confirm the efficiency of the proposed approach.

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225 , Gdynia, Poland

    Dariusz Barbucha

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  1. Dariusz Barbucha
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Correspondence to Dariusz Barbucha .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. KES International Research, UK

    Robert J. Howlett

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Barbucha, D. (2020). Multi-agent Approach to the DVRP with GLS Improvement Procedure. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_10

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